Real-time weather information is of vital importance for a world that is increasingly mobile. In response to this, a variety of techniques have been created to communicate real-time weather information. One popular and pervasive technique uses radar.
With radar bursts of energy waves are transmitted from a source transmitter out over a large geographical area within the atmosphere. When the energy waves encounter the presence of matter in the atmosphere, small portions of the transmitted energy waves are reflected back to a receiver usually co-located with the source. The source/receiver can then process this reflected energy to determine what type of matter was encountered within the atmosphere, such as predefined weather phenomena (e.g., precipitation, degrees of precipitation, etc.) Some reflected energy may be spurious scatterers representing such things as birds, insects, planes, and the like. These spurious reflections may be disregarded. It should be noted that source/receivers do not have to be stationary devices. That is, in some cases, aircraft may be equipped with radar source/receiver equipment.
The reflected energy readings are transmitted over radio or satellite channels as weather radar reflectivity data. This data is voluminous and transmitted as values representing decibels of Z (dBZ). Z represents the normalized amount of energy reflected back from weather phenomena within the atmosphere.
One area where weather phenomena are of vital importance is aviation. The most common reason for delaying flights is weather conditions. In order for weather information to be useful to a pilot of an aircraft it is transmitted via ground-to-aircraft (ground-to-air) wireless communication links. However, radar reflectivity data is voluminous, such that a ground-to-air wireless communication link's bandwidth can quickly become overburdened and taxed to capacity during a transmission of radar reflectivity data. Moreover, wireless ground-to-air bandwidth is a precious resource that is closely guarded and managed during a flight. Demands for high ground-to-air and/or air-to-ground bandwidth entail large operational expenditures for airlines and other users.
Two popular data compression techniques have been used to address data compression. The first approach is referred to as lossy compression. With lossy compression some data is lost during compression and is not recoverable when it is subsequently decompressed. The benefit of lossy compression is that an original data file may experience a larger reduction in its size when it is compressed. The drawback of lossy compression is that when the data file is decompressed some vital information may not be present and may not be recoverable. Thus, lossy compression is often not acceptable to pilots because image quality associated with weather phenomena is degraded with lossy compression techniques.
A second approach is lossless compression. With lossless compression a compressed data file is fully recovered when it is subsequently decompressed. Lossless compression is popular with the compression associated with text files, word processing files, spreadsheet files, and the like where any loss of data can be considered catastrophic from a user's point of view. The drawback of lossless compression is that the size of the data file is not substantially reduced when compressed. Therefore, lossless compression is often not acceptable because it does not maximally address the bandwidth problem associated with transmitting ground-to-air weather radar reflectivity data.
A variety of other approaches have been attempted that yield compression performance between the extremes of lossy compression and lossless compression. For example, techniques have attempted to reduce spatial redundancy within a compressed data file. However, these techniques have generally only been capable of reducing the size of an original data file by a factor of about 10. Other techniques have used vector quantization to exploit linear and nonlinear relationships within a data file being compressed; but these techniques suffer from a lack of universal definitions needed to effectively compress and decompress a variety of data types. In still other techniques, there have been attempts to use progressive image transmission (PIT) schemes for transmitting the most significant information from a data file sequentially until it can be successfully reconstructed. PIT techniques fail to communicate sufficient boundary information and internal details during initial transmissions, though the picture is reconstructed well when the data transmission is complete.
Recently, schemes for context based image transmission (CBIT) have emerged for medical images. These schemes utilize knowledge about image composition to segment, label, prioritize, and fit geometric models to regions of an image. However, these schemes are not readily adapted to radar data due to the complexity and randomness of weather data fields, and the lack of prior knowledge about the dynamic shapes that can be associated with weather features (weather patterns).
Still other techniques have been proposed, but none of these can achieve sufficient data size reduction on radar data while retaining an acceptable level of quality, and none of these can sufficiently address the randomness associated with shapes of weather patterns represented within radar data.
Therefore, there is a need for an improved data compression and decompression technique for weather radar reflectivity data.